{"id":"W4401525591","doi":"10.1016/j.heliyon.2024.e36163","title":"Antiprotozoal peptide prediction using machine learning with effective feature selection techniques","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University Grants Commission","keywords":"Feature selection; Antiprotozoal; Machine learning; Artificial intelligence; Selection (genetic algorithm); Computer science; Feature (linguistics); Chemistry; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001290947,0.0001796185,0.0001750309,0.0001463358,0.0002906326,0.00006118728,0.00004773369,0.0002201893,0.00009951356],"category_scores_gemma":[0.00001285803,0.0001376952,0.00006473978,0.0001887182,0.00009988132,0.0002444166,0.00003036626,0.0006606937,0.00006039134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008370999,"about_ca_system_score_gemma":0.00003628422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001326814,"about_ca_topic_score_gemma":0.00004902831,"domain_scores_codex":[0.9992098,0.000118573,0.0001027993,0.0002958219,0.00003137356,0.0002416286],"domain_scores_gemma":[0.9997801,0.00004911087,0.00004499541,0.00006900975,0.00004513644,0.00001161751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002040574,0.00002853598,0.006893408,0.0002961326,0.0001336475,0.00001045902,0.0002126652,0.00004735292,0.9849311,0.0001001415,0.0003358819,0.006806601],"study_design_scores_gemma":[0.000253391,0.000712615,0.001551521,0.001398183,0.00008910178,0.0006968409,0.0001110657,0.0004195163,0.9351695,0.00001398212,0.05937243,0.0002119012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682658,0.01218942,0.01437993,0.0001701973,0.0005983124,0.001214972,0.00007924082,0.001508249,0.00159388],"genre_scores_gemma":[0.9910907,0.0004771671,0.001428095,0.00004530786,0.0001851824,0.00005739436,0.0001084719,0.0000452355,0.006562483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05903655,"threshold_uncertainty_score":0.5615046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005932030019915671,"score_gpt":0.2273984859990468,"score_spread":0.2214664559791312,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}